Preprocessing Microbiome Data with Phyloseq

Preprocessing Overview

In this section we will take raw microbiome count data and turn it into a clean, analysis-ready phyloseq object. You will:

1. Load your core inputs

• A rooted phylogenetic tree (refs_tree.treefile)
  • produced in the microbiome_references tutorial.
• Representative 16S sequences (refs_aligned_mafft.fasta)
  • produced in the microbiome_references tutorial.
• A taxonomic assignment table (tax_table.RData)
  • produced in the microbiome_alignments tutorial.
• Sample metadata (sample_table.RData)
  • produced in the metadata_setup tutorial and then prepared for microbiomes in [microbiome_alignments]((https://rich-molecular-health-lab.github.io/tutorials/microbiome_alignments.html).
• An OTU count table (otu_table.RData)
  • produced in the microbiome_alignments tutorial.

2. Merge replicate samples

• Pool all reads for the same subject on the same day into a single sample. OTU counts are summed, and numeric metadata are averaged.

3. Clean up taxonomy & prune

• Rename each tip to a simple “OTU1…OTUn” label
• Remove very low-abundance OTUs (e.g. total count ≤ 1)
• Collapse your count table and tree to the Genus rank
• Perform a second pruning step so that only genera with meaningful counts remain

4. Save analysis-ready objects

• A cleaned, genus-level phyloseq object (phyloseq_genus.RData)
• Its melted version (phyloseq_melt_genus.RData) for ggplot workflows
• The pruned, genus-level tree (tree_genus.RData)
• Analogous objects for Family, Order, Class, and Phylum

By the end of this stage, you will have one phyloseq object per taxonomic level, each containing:

• A consistent OTU (or Genus/Family…) count table
• Matched sample metadata and taxonomic table
• A pruned phylogenetic tree

Packages Used

In this script we mostly use the phyloseq package, designed for microbiome analysis.

phyloseq Utility

• Integrates OTU/ASV tables, sample metadata, taxonomy, phylogenetic trees, and (optionally) reference sequences in one S4 object.
• Provides convenient methods for merging, pruning, rarefaction, distance calculations, and plotting.
• Under the hood, relies on Bioconductor classes (DataFrames, XStringSets, phylo).

Note: there is much overlap between phyloseq, microeco, and microbiome, but each makes some steps more streamlined than others, so I prefer to switch between them for different stages of analysis.

We will also use the treedataverse package suite for some visualization to check in on our data quality.

treedataverse Utility

The treedataverse suite provides tools for managing, sharing, and visualizing phylogenetic trees in R. It builds on the Dataverse platform to store trees (and associated data) in versioned, citable repositories. With treedataverse, you can:

• Upload or download phylo objects directly from a Dataverse collection
• Attach rich metadata (e.g. study details, taxon provenance) to each tree
• Track updates and collaborate on tree data with full provenance
• Integrate seamlessly with ggtree and ape for downstream plotting and analysis

ggtree Package within the treedataverse

ggtree is an extension of ggplot2 tailored for phylogenetic trees. It lets you:

• Map tree data (branches, node labels, support values) to aesthetic scales
• Annotate clades with heatmaps, bar charts, or tip points (e.g. taxon abundances)
• Collapse or expand subtrees interactively
• Align associated data (e.g. trait matrices) alongside your tree in multi-panel layouts
• Leverage the full grammar of graphics for publication-quality figures

---

Helper Functions

I created the functions below because I repeatedly use these code chunks to view summaries of my data after each step of processing.

my_skim <- skim_with(numeric  = sfl(p25 = NULL, p50 = NULL, p75 = NULL))

skim_phyloseq <- function(phyloseq) {
options(
  digits            = 2,
  pillar.sigfig     = 2,
  pillar.subtle_num = TRUE
  )
 skim <- psmelt(phyloseq) %>% 
    select(-c(
      subject_certainty, 
      subject_studbook_id, 
      subject_dam_id, 
      subject_sire_id, 
      subject_birth_location, 
      subject_age)) %>%
    group_by(subject) %>% 
    my_skim() %>%
    select(-complete_rate)
 
 return(skim)
}

view_tree <- function(phyloseq, level = NULL, palette = "rcartocolor::Prism") {
  phylum.cols <-  sample(paletteer::paletteer_d(paste0(palette)), size = length(get_taxa_unique(phyloseq, "Phylum")), replace = TRUE) %>%
    set_names(., get_taxa_unique(phyloseq, "Phylum"))
  
  tree1 <- ggtree(phyloseq, branch.length = "none", aes(color = Phylum))
  
  if (is.null(level)) {
    tree2 <- tree1
  } else if (level == "Class") {
    tree2 <- tree1 +
      geom_label(aes(label = Class, fill = Phylum), hjust = -0.05, size = 2, colour = "#000000FF")
  } else if (level == "Phylum") {
    tree2 <- tree1 +
      geom_label(aes(label = Phylum, fill = Phylum), hjust = -0.05, size = 3, colour = "#000000FF")
  }
  
  tree.out <- tree2 +
    scale_color_manual(values = phylum.cols, na.value = "#00000080") +
    scale_fill_manual( values = alpha(c(phylum.cols), 0.3)) +
    theme_tree(legend.position = "none")
  
  return(tree.out)
}

---

Load Data

Note that I use .RData files to load some of the data below. You could load these same objects in the form of more widely applicable files, if you wish (see below for explaination).

What is an .RData file?

An .RData (or sometimes .rda) file is R’s native, binary workspace format. When you call save() on one or more R objects—data frames, matrices, lists, S4/S3 objects, etc.—R serializes them into a compact, platform-independent file. You can later restore those exact objects (with their names and attributes intact) by calling load(“your_file.RData”). Using .RData is an efficient way to cache intermediate steps, share processed datasets with collaborators, or speed up analyses by skipping time-consuming recomputation.

tree <- read.tree("microeco/loris/refs_tree.treefile")
seqs <- readDNAStringSet("microeco/loris/refs_aligned_mafft.fasta")
load("microeco/loris/tax_table.RData")
load("microeco/loris/sample_table.RData")
load("microeco/loris/otu_table.RData")

Sample Metadata as Factors

Phyloseq will only capture numeric/factor values (not strings) for the metadata, so we need to reformat our character columns.

sample_data <- sample.table %>%
  arrange(subject, collection_day) %>%
  mutate(subject_certainty       = fct_recode(subject_certainty  , "0" = "no" , "1" = "yes"),
         environment_holding     = fct_recode(environment_holding, "0" = "old", "1" = "new"),
         environment_pair_access = fct_recode(environment_pair_access, "0" = "n", "1" = "y"),
         repro_warb_status       = fct_recode(repro_warb_status, "0" = "anestrus", "1" = "estrus")) %>%
  mutate(across(where(is.character), ~factor(.))) %>%
  mutate(diet_name = fct_inorder(diet_name))

Phyloseq Object

phylo <- phyloseq(
  tax_table(as.matrix(tax.table)),
  otu_table(as.matrix(otu.table), taxa_are_rows = TRUE),
  sample_data(sample_data),
  phy_tree(tree),
  refseq(seqs)
)

Preview Starting Data

skim_phyloseq(phylo)

Data summary

Name	Piped data
Number of rows	906576
Number of columns	87
_______________________
Column type frequency:
character	9
Date	1
factor	6
numeric	70
________________________
Group variables	subject

Variable type: character

skim_variable	subject	n_missing	min	max	empty	n_unique	whitespace
OTU	culi	0	6	11	0	2244	0
OTU	warble	0	6	11	0	2244	0
Sample	culi	0	10	15	0	175	0
Sample	warble	0	10	15	0	229	0
Kingdom	culi	0	11	11	0	1	0
Kingdom	warble	0	11	11	0	1	0
Phylum	culi	0	13	29	0	26	0
Phylum	warble	0	13	29	0	26	0
Class	culi	0	3	26	0	56	0
Class	warble	0	3	26	0	56	0
Order	culi	0	3	26	0	128	0
Order	warble	0	3	26	0	128	0
Family	culi	0	3	32	0	273	0
Family	warble	0	3	32	0	273	0
Genus	culi	0	3	26	0	925	0
Genus	warble	0	3	26	0	925	0
Species	culi	0	15	46	0	2244	0
Species	warble	0	15	46	0	2244	0

Variable type: Date

skim_variable	subject	n_missing	min	max	median	n_unique
collection_date	culi	0	2023-10-26	2024-12-14	2024-02-04	166
collection_date	warble	0	2023-10-26	2024-09-15	2024-03-26	219

Variable type: factor

skim_variable	subject	n_missing	ordered	n_unique	top_counts
subject_sex	culi	0	FALSE	1	m: 392700, f: 0
subject_sex	warble	0	FALSE	1	f: 513876, m: 0
subject_day	culi	0	FALSE	166	cul: 4488, cul: 4488, cul: 4488, cul: 4488
subject_day	warble	0	FALSE	219	war: 4488, war: 4488, war: 4488, war: 4488
environment_holding	culi	0	FALSE	2	0: 246840, 1: 145860
environment_holding	warble	0	FALSE	2	1: 302940, 0: 210936
environment_pair_access	culi	0	FALSE	2	0: 359040, 1: 33660
environment_pair_access	warble	0	FALSE	2	0: 489192, 1: 24684
repro_warb_status	culi	0	FALSE	3	0: 296208, pre: 53856, 1: 42636
repro_warb_status	warble	0	FALSE	3	0: 235620, pre: 235620, 1: 42636
diet_name	culi	0	FALSE	3	oat: 249084, bas: 141372, bis: 2244
diet_name	warble	0	FALSE	1	bas: 513876, oat: 0, bis: 0

Variable type: numeric

skim_variable	subject	n_missing	mean	sd	p0	p100	hist
Abundance	culi	0	3.21	62.37	0.00	7168.00	▇▁▁▁▁
Abundance	warble	0	2.72	56.75	0.00	8486.00	▇▁▁▁▁
collection_day	culi	0	100.13	62.10	1.00	416.00	▇▇▂▁▁
collection_day	warble	0	161.90	96.25	1.00	326.00	▇▇▇▆▇
bristol_min	culi	8976	3.11	1.03	0.00	6.00	▁▂▇▁▂
bristol_min	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
bristol_mean	culi	0	3.49	1.18	0.00	6.00	▁▃▇▅▂
bristol_mean	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
bristol_max	culi	8976	3.85	1.56	0.00	6.00	▁▃▇▁▇
bristol_max	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
supplement_probiotic	culi	0	0.71	0.44	0.00	1.00	▃▁▁▁▇
supplement_probiotic	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
supplement_steroid	culi	0	0.69	0.43	0.00	1.00	▃▁▂▁▇
supplement_steroid	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
supplement_fiber	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
supplement_fiber	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
supplement_antibiotic	culi	0	0.02	0.13	0.00	1.00	▇▁▁▁▁
supplement_antibiotic	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
supplement_antidiarrheal	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
supplement_antidiarrheal	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Total_total	culi	0	0.60	0.04	0.54	0.73	▅▁▇▁▁
diet_Total_total	warble	0	0.54	0.00	0.54	0.54	▁▁▇▁▁
diet_Protein_total	culi	0	0.67	0.02	0.60	0.69	▁▁▁▇▅
diet_Protein_total	warble	0	0.69	0.00	0.69	0.69	▁▁▇▁▁
diet_Fat_total	culi	0	0.62	0.01	0.61	0.70	▇▅▁▁▁
diet_Fat_total	warble	0	0.63	0.00	0.63	0.63	▁▁▇▁▁
diet_CHO_by_diff_total	culi	0	0.34	0.04	0.29	0.60	▅▇▁▁▁
diet_CHO_by_diff_total	warble	0	0.29	0.00	0.29	0.29	▁▁▇▁▁
diet_Ash_total	culi	0	1.00	0.02	0.79	1.00	▁▁▁▁▇
diet_Ash_total	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
diet_Total_dry_matter	culi	0	0.56	0.01	0.53	0.56	▁▁▁▅▇
diet_Total_dry_matter	warble	0	0.55	0.00	0.55	0.55	▁▁▇▁▁
diet_Total_kcal	culi	0	0.97	0.00	0.97	0.97	▁▁▇▁▁
diet_Total_kcal	warble	0	0.97	0.00	0.97	0.97	▁▁▇▁▁
diet_Foods_biscuit_rotation	culi	0	0.23	0.02	0.00	0.25	▁▁▁▁▇
diet_Foods_biscuit_rotation	warble	0	0.25	0.00	0.25	0.25	▁▁▇▁▁
diet_Foods_invertebrate_misc	culi	0	0.15	0.03	0.13	0.26	▇▁▅▁▁
diet_Foods_invertebrate_misc	warble	0	0.19	0.00	0.19	0.19	▁▁▇▁▁
diet_Foods_mazuri_enrich_gum_arabic_5b35	culi	0	0.21	0.01	0.20	0.24	▇▁▁▅▁
diet_Foods_mazuri_enrich_gum_arabic_5b35	warble	0	0.23	0.00	0.23	0.23	▁▁▇▁▁
diet_Foods_protein_rotation	culi	0	0.04	0.00	0.04	0.07	▇▁▁▁▁
diet_Foods_protein_rotation	warble	0	0.04	0.00	0.04	0.04	▁▁▇▁▁
diet_Foods_seasonal_vegetables	culi	0	0.32	0.02	0.29	0.43	▅▇▁▁▁
diet_Foods_seasonal_vegetables	warble	0	0.29	0.00	0.29	0.29	▁▁▇▁▁
diet_Foods_egg_whole_cooked_meat_prep	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_egg_whole_cooked_meat_prep	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_potato_sweet	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_potato_sweet	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_bean_green_fresh	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_bean_green_fresh	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_carrot	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_carrot	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_pumpkin_canned	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_pumpkin_canned	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_egg_whole_raw_meat_prep	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_egg_whole_raw_meat_prep	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_root	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_root	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_non_root	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_non_root	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_egg_white_only_cooked	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_egg_white_only_cooked	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_potato_sweet_cooked	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_potato_sweet_cooked	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_celery	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_celery	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_lettuce_romaine	culi	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_lettuce_romaine	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Foods_ohdz_oatmeal_gel	culi	0	0.05	0.04	0.00	0.08	▅▁▁▁▇
diet_Foods_ohdz_oatmeal_gel	warble	0	0.00	0.00	0.00	0.00	▁▁▇▁▁
diet_Protein_taurine	culi	0	0.99	0.08	0.00	1.00	▁▁▁▁▇
diet_Protein_taurine	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
diet_Protein_methionine	culi	0	0.44	0.01	0.32	0.45	▁▁▁▁▇
diet_Protein_methionine	warble	0	0.45	0.00	0.45	0.45	▁▁▇▁▁
diet_Fat_omega_3_ala__epa__dha	culi	0	0.99	0.08	0.00	1.00	▁▁▁▁▇
diet_Fat_omega_3_ala__epa__dha	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
diet_Fat_omega_6_la__gla__aa	culi	0	0.99	0.08	0.00	1.00	▁▁▁▁▇
diet_Fat_omega_6_la__gla__aa	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
diet_CHO_by_diff_starch	culi	0	0.95	0.07	0.38	1.00	▁▁▁▁▇
diet_CHO_by_diff_starch	warble	0	0.88	0.00	0.88	0.88	▁▁▇▁▁
diet_CHO_by_diff_ndf	culi	0	0.78	0.01	0.75	0.78	▁▁▅▁▇
diet_CHO_by_diff_ndf	warble	0	0.77	0.00	0.77	0.77	▁▁▇▁▁
diet_CHO_by_diff_adf	culi	0	0.99	0.05	0.31	1.00	▁▁▁▁▇
diet_CHO_by_diff_adf	warble	0	0.97	0.00	0.97	0.97	▁▁▇▁▁
diet_CHO_by_diff_crude_fiber	culi	0	0.57	0.01	0.57	0.71	▇▁▁▁▁
diet_CHO_by_diff_crude_fiber	warble	0	0.57	0.00	0.57	0.57	▁▁▇▁▁
diet_CHO_by_diff_tdf	culi	0	0.41	0.04	0.38	0.76	▇▁▁▁▁
diet_CHO_by_diff_tdf	warble	0	0.38	0.00	0.38	0.38	▁▁▇▁▁
diet_Ash_calcium	culi	0	0.96	0.01	0.77	0.97	▁▁▁▁▇
diet_Ash_calcium	warble	0	0.97	0.00	0.97	0.97	▁▁▇▁▁
diet_Ash_phosphorus	culi	0	0.96	0.03	0.94	1.00	▇▁▁▁▅
diet_Ash_phosphorus	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
diet_Ash_potassium	culi	0	0.88	0.00	0.88	0.91	▇▁▁▁▁
diet_Ash_potassium	warble	0	0.88	0.00	0.88	0.88	▁▁▇▁▁
diet_Ash_magnesium	culi	0	0.50	0.05	0.44	0.72	▅▇▁▁▁
diet_Ash_magnesium	warble	0	0.44	0.00	0.44	0.44	▁▁▇▁▁
diet_Ash_copper	culi	0	0.99	0.05	0.31	1.00	▁▁▁▁▇
diet_Ash_copper	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
diet_Ash_iron	culi	0	0.98	0.02	0.79	1.00	▁▁▁▁▇
diet_Ash_iron	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
diet_Ash_zinc	culi	0	0.98	0.04	0.47	1.00	▁▁▁▁▇
diet_Ash_zinc	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
diet_Ash_manganese	culi	0	0.99	0.06	0.24	1.00	▁▁▁▁▇
diet_Ash_manganese	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
diet_Ash_iodine	culi	0	0.99	0.08	0.00	1.00	▁▁▁▁▇
diet_Ash_iodine	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
diet_Ash_ca_p	culi	0	0.84	0.02	0.69	0.86	▁▁▁▅▇
diet_Ash_ca_p	warble	0	0.81	0.00	0.81	0.81	▁▁▇▁▁
diet_Vitamins_vit_a_iu	culi	0	0.25	0.02	0.23	0.27	▅▁▁▁▇
diet_Vitamins_vit_a_iu	warble	0	0.23	0.00	0.23	0.23	▁▁▇▁▁
diet_Vitamins_vit_d3	culi	0	0.98	0.02	0.73	0.98	▁▁▁▁▇
diet_Vitamins_vit_d3	warble	0	0.98	0.00	0.98	0.98	▁▁▇▁▁
diet_Vitamins_vit_e	culi	0	0.93	0.10	0.64	1.00	▁▁▅▁▇
diet_Vitamins_vit_e	warble	0	0.80	0.00	0.80	0.80	▁▁▇▁▁
diet_Vitamins_vit_k	culi	0	0.97	0.04	0.40	0.98	▁▁▁▁▇
diet_Vitamins_vit_k	warble	0	0.96	0.00	0.96	0.96	▁▁▇▁▁
diet_Vitamins_vit_b1_thiamin	culi	0	0.98	0.05	0.28	0.99	▁▁▁▁▇
diet_Vitamins_vit_b1_thiamin	warble	0	0.98	0.00	0.98	0.98	▁▁▇▁▁
diet_Vitamins_vit_b2_riboflavin	culi	0	0.91	0.03	0.68	0.94	▁▁▁▇▅
diet_Vitamins_vit_b2_riboflavin	warble	0	0.94	0.00	0.94	0.94	▁▁▇▁▁
diet_Vitamins_vit_b3_niacin	culi	0	0.99	0.04	0.49	1.00	▁▁▁▁▇
diet_Vitamins_vit_b3_niacin	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
diet_Vitamins_choline	culi	0	0.50	0.01	0.46	0.52	▁▁▇▁▅
diet_Vitamins_choline	warble	0	0.52	0.00	0.52	0.52	▁▁▇▁▁
diet_Vitamins_vit_b5_pantothenic_acid	culi	0	0.92	0.04	0.39	0.94	▁▁▁▁▇
diet_Vitamins_vit_b5_pantothenic_acid	warble	0	0.94	0.00	0.94	0.94	▁▁▇▁▁
diet_Vitamins_vit_b6_pyridoxine	culi	0	0.96	0.05	0.35	0.96	▁▁▁▁▇
diet_Vitamins_vit_b6_pyridoxine	warble	0	0.96	0.00	0.96	0.96	▁▁▇▁▁
diet_Vitamins_vit_b7_biotin	culi	0	0.99	0.02	0.72	1.00	▁▁▁▁▇
diet_Vitamins_vit_b7_biotin	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
diet_Vitamins_folic_acid	culi	0	0.99	0.07	0.05	1.00	▁▁▁▁▇
diet_Vitamins_folic_acid	warble	0	1.00	0.00	1.00	1.00	▁▁▇▁▁
diet_Vitamins_vit_b12	culi	0	0.50	0.07	0.45	1.00	▇▅▁▁▁
diet_Vitamins_vit_b12	warble	0	0.56	0.00	0.56	0.56	▁▁▇▁▁
diet_Vitamins_vit_c	culi	0	0.97	0.04	0.65	1.00	▁▁▁▁▇
diet_Vitamins_vit_c	warble	0	0.93	0.00	0.93	0.93	▁▁▇▁▁
diet_Vitamins_beta_carotene	culi	0	0.21	0.02	0.18	0.24	▅▁▁▇▁
diet_Vitamins_beta_carotene	warble	0	0.18	0.00	0.18	0.18	▁▁▇▁▁
diet_Vitamins_lycopene	culi	0	0.40	0.06	0.32	0.65	▅▇▁▁▁
diet_Vitamins_lycopene	warble	0	0.32	0.00	0.32	0.32	▁▁▇▁▁

view_tree(phylo) + ggtitle("Species-Level OTUs before Filtering")

That looks like some very dense, messy taxonomic data!

---

Dereplicate Samples

First we will create a version that merges the replicated samples so that we have one observation per subject per day. This function’s default in phyloseq will aggregate the OTU counts across multiple samples as a sum of reads while taking mean values for everything in the metadata.

This pooling step creates one composite sample per subject per day, avoiding pseudo‐replication in downstream diversity metrics.

phylo_merged <- phylo %>% merge_samples("subject_day")

skim_phyloseq(phylo_merged)

Data summary

Name	Piped data
Number of rows	863940
Number of columns	87
_______________________
Column type frequency:
character	9
numeric	77
________________________
Group variables	subject

Variable type: character

skim_variable	subject	n_missing	min	max	empty	n_unique	whitespace
OTU	1	0	6	11	0	2244	0
OTU	2	0	6	11	0	2244	0
Sample	1	0	6	8	0	166	0
Sample	2	0	8	10	0	219	0
Kingdom	1	0	11	11	0	1	0
Kingdom	2	0	11	11	0	1	0
Phylum	1	0	13	29	0	26	0
Phylum	2	0	13	29	0	26	0
Class	1	0	3	26	0	56	0
Class	2	0	3	26	0	56	0
Order	1	0	3	26	0	128	0
Order	2	0	3	26	0	128	0
Family	1	0	3	32	0	273	0
Family	2	0	3	32	0	273	0
Genus	1	0	3	26	0	925	0
Genus	2	0	3	26	0	925	0
Species	1	0	15	46	0	2244	0
Species	2	0	15	46	0	2244	0

Variable type: numeric

skim_variable	subject	n_missing	mean	sd	p0	p100	hist
Abundance	1	0	3.4e+00	65.57	0.0e+00	7.2e+03	▇▁▁▁▁
Abundance	2	0	2.9e+00	58.71	0.0e+00	8.5e+03	▇▁▁▁▁
subject_sex	1	0	2.0e+00	0.00	2.0e+00	2.0e+00	▁▁▇▁▁
subject_sex	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
subject_day	1	0	8.4e+01	47.92	1.0e+00	1.7e+02	▇▇▇▇▇
subject_day	2	0	2.8e+02	63.22	1.7e+02	3.8e+02	▇▇▇▇▇
collection_day	1	0	1.0e+02	62.09	1.0e+00	4.2e+02	▇▇▂▁▁
collection_day	2	0	1.6e+02	95.94	1.0e+00	3.3e+02	▇▇▇▆▇
collection_date	1	0	2.0e+04	62.09	2.0e+04	2.0e+04	▇▇▂▁▁
collection_date	2	0	2.0e+04	95.94	2.0e+04	2.0e+04	▇▇▇▆▇
environment_holding	1	0	1.6e+00	0.48	1.0e+00	2.0e+00	▅▁▁▁▇
environment_holding	2	0	1.4e+00	0.49	1.0e+00	2.0e+00	▇▁▁▁▆
environment_pair_access	1	0	1.1e+00	0.29	1.0e+00	2.0e+00	▇▁▁▁▁
environment_pair_access	2	0	1.1e+00	0.22	1.0e+00	2.0e+00	▇▁▁▁▁
repro_warb_status	1	0	1.4e+00	0.73	1.0e+00	3.0e+00	▇▁▁▁▂
repro_warb_status	2	0	2.0e+00	0.96	1.0e+00	3.0e+00	▇▁▂▁▇
bristol_min	1	8976	3.1e+00	1.02	0.0e+00	6.0e+00	▁▂▇▁▂
bristol_min	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
bristol_mean	1	0	3.5e+00	1.17	0.0e+00	6.0e+00	▁▃▇▃▂
bristol_mean	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
bristol_max	1	8976	3.8e+00	1.55	0.0e+00	6.0e+00	▁▃▇▁▆
bristol_max	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
supplement_probiotic	1	0	7.1e-01	0.44	0.0e+00	1.0e+00	▃▁▁▁▇
supplement_probiotic	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_steroid	1	0	7.0e-01	0.43	0.0e+00	1.0e+00	▃▁▂▁▇
supplement_steroid	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_fiber	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_fiber	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_antibiotic	1	0	2.0e-02	0.13	0.0e+00	1.0e+00	▇▁▁▁▁
supplement_antibiotic	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_antidiarrheal	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_antidiarrheal	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_name	1	0	1.7e+00	0.49	1.0e+00	3.0e+00	▅▁▇▁▁
diet_name	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Total_total	1	0	6.0e-01	0.04	5.4e-01	7.3e-01	▅▁▇▁▁
diet_Total_total	2	0	5.4e-01	0.00	5.4e-01	5.4e-01	▁▁▇▁▁
diet_Protein_total	1	0	6.7e-01	0.02	6.0e-01	6.9e-01	▁▁▁▇▅
diet_Protein_total	2	0	6.9e-01	0.00	6.9e-01	6.9e-01	▁▁▇▁▁
diet_Fat_total	1	0	6.2e-01	0.01	6.1e-01	7.0e-01	▇▅▁▁▁
diet_Fat_total	2	0	6.3e-01	0.00	6.3e-01	6.3e-01	▁▁▇▁▁
diet_CHO_by_diff_total	1	0	3.4e-01	0.04	2.9e-01	6.0e-01	▅▇▁▁▁
diet_CHO_by_diff_total	2	0	2.9e-01	0.00	2.9e-01	2.9e-01	▁▁▇▁▁
diet_Ash_total	1	0	1.0e+00	0.02	7.9e-01	1.0e+00	▁▁▁▁▇
diet_Ash_total	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Total_dry_matter	1	0	5.6e-01	0.01	5.3e-01	5.6e-01	▁▁▁▅▇
diet_Total_dry_matter	2	0	5.5e-01	0.00	5.5e-01	5.5e-01	▁▁▇▁▁
diet_Total_kcal	1	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_Total_kcal	2	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_Foods_biscuit_rotation	1	0	2.3e-01	0.02	0.0e+00	2.5e-01	▁▁▁▁▇
diet_Foods_biscuit_rotation	2	0	2.5e-01	0.00	2.5e-01	2.5e-01	▁▁▇▁▁
diet_Foods_invertebrate_misc	1	0	1.5e-01	0.03	1.3e-01	2.6e-01	▇▁▅▁▁
diet_Foods_invertebrate_misc	2	0	1.9e-01	0.00	1.9e-01	1.9e-01	▁▁▇▁▁
diet_Foods_mazuri_enrich_gum_arabic_5b35	1	0	2.1e-01	0.01	2.0e-01	2.4e-01	▇▁▁▅▁
diet_Foods_mazuri_enrich_gum_arabic_5b35	2	0	2.3e-01	0.00	2.3e-01	2.3e-01	▁▁▇▁▁
diet_Foods_protein_rotation	1	0	4.0e-02	0.00	4.0e-02	7.0e-02	▇▁▁▁▁
diet_Foods_protein_rotation	2	0	4.0e-02	0.00	4.0e-02	4.0e-02	▁▁▇▁▁
diet_Foods_seasonal_vegetables	1	0	3.2e-01	0.02	2.9e-01	4.3e-01	▅▇▁▁▁
diet_Foods_seasonal_vegetables	2	0	2.9e-01	0.00	2.9e-01	2.9e-01	▁▁▇▁▁
diet_Foods_egg_whole_cooked_meat_prep	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_whole_cooked_meat_prep	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_bean_green_fresh	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_bean_green_fresh	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_carrot	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_carrot	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_pumpkin_canned	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_pumpkin_canned	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_whole_raw_meat_prep	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_whole_raw_meat_prep	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_root	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_root	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_non_root	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_non_root	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_white_only_cooked	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_white_only_cooked	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet_cooked	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet_cooked	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_celery	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_celery	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_lettuce_romaine	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_lettuce_romaine	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_ohdz_oatmeal_gel	1	0	5.0e-02	0.04	0.0e+00	8.0e-02	▅▁▁▁▇
diet_Foods_ohdz_oatmeal_gel	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Protein_taurine	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Protein_taurine	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Protein_methionine	1	0	4.4e-01	0.01	3.2e-01	4.5e-01	▁▁▁▁▇
diet_Protein_methionine	2	0	4.5e-01	0.00	4.5e-01	4.5e-01	▁▁▇▁▁
diet_Fat_omega_3_ala__epa__dha	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Fat_omega_3_ala__epa__dha	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Fat_omega_6_la__gla__aa	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Fat_omega_6_la__gla__aa	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_CHO_by_diff_starch	1	0	9.5e-01	0.07	3.8e-01	1.0e+00	▁▁▁▁▇
diet_CHO_by_diff_starch	2	0	8.8e-01	0.00	8.8e-01	8.8e-01	▁▁▇▁▁
diet_CHO_by_diff_ndf	1	0	7.8e-01	0.01	7.5e-01	7.8e-01	▁▁▅▁▇
diet_CHO_by_diff_ndf	2	0	7.7e-01	0.00	7.7e-01	7.7e-01	▁▁▇▁▁
diet_CHO_by_diff_adf	1	0	9.9e-01	0.05	3.1e-01	1.0e+00	▁▁▁▁▇
diet_CHO_by_diff_adf	2	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_CHO_by_diff_crude_fiber	1	0	5.7e-01	0.01	5.7e-01	7.1e-01	▇▁▁▁▁
diet_CHO_by_diff_crude_fiber	2	0	5.7e-01	0.00	5.7e-01	5.7e-01	▁▁▇▁▁
diet_CHO_by_diff_tdf	1	0	4.1e-01	0.04	3.8e-01	7.6e-01	▇▁▁▁▁
diet_CHO_by_diff_tdf	2	0	3.8e-01	0.00	3.8e-01	3.8e-01	▁▁▇▁▁
diet_Ash_calcium	1	0	9.6e-01	0.01	7.7e-01	9.7e-01	▁▁▁▁▇
diet_Ash_calcium	2	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_Ash_phosphorus	1	0	9.6e-01	0.03	9.4e-01	1.0e+00	▇▁▁▁▅
diet_Ash_phosphorus	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_potassium	1	0	8.8e-01	0.00	8.8e-01	9.1e-01	▇▁▁▁▁
diet_Ash_potassium	2	0	8.8e-01	0.00	8.8e-01	8.8e-01	▁▁▇▁▁
diet_Ash_magnesium	1	0	5.0e-01	0.05	4.4e-01	7.2e-01	▅▇▁▁▁
diet_Ash_magnesium	2	0	4.4e-01	0.00	4.4e-01	4.4e-01	▁▁▇▁▁
diet_Ash_copper	1	0	9.9e-01	0.05	3.1e-01	1.0e+00	▁▁▁▁▇
diet_Ash_copper	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_iron	1	0	9.8e-01	0.02	7.9e-01	1.0e+00	▁▁▁▁▇
diet_Ash_iron	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_zinc	1	0	9.8e-01	0.04	4.7e-01	1.0e+00	▁▁▁▁▇
diet_Ash_zinc	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_manganese	1	0	9.9e-01	0.06	2.4e-01	1.0e+00	▁▁▁▁▇
diet_Ash_manganese	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_iodine	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Ash_iodine	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_ca_p	1	0	8.4e-01	0.02	6.9e-01	8.6e-01	▁▁▁▅▇
diet_Ash_ca_p	2	0	8.1e-01	0.00	8.1e-01	8.1e-01	▁▁▇▁▁
diet_Vitamins_vit_a_iu	1	0	2.5e-01	0.02	2.3e-01	2.7e-01	▅▁▁▁▇
diet_Vitamins_vit_a_iu	2	0	2.3e-01	0.00	2.3e-01	2.3e-01	▁▁▇▁▁
diet_Vitamins_vit_d3	1	0	9.8e-01	0.02	7.3e-01	9.8e-01	▁▁▁▁▇
diet_Vitamins_vit_d3	2	0	9.8e-01	0.00	9.8e-01	9.8e-01	▁▁▇▁▁
diet_Vitamins_vit_e	1	0	9.3e-01	0.10	6.4e-01	1.0e+00	▁▁▅▁▇
diet_Vitamins_vit_e	2	0	8.0e-01	0.00	8.0e-01	8.0e-01	▁▁▇▁▁
diet_Vitamins_vit_k	1	0	9.7e-01	0.04	4.0e-01	9.8e-01	▁▁▁▁▇
diet_Vitamins_vit_k	2	0	9.6e-01	0.00	9.6e-01	9.6e-01	▁▁▇▁▁
diet_Vitamins_vit_b1_thiamin	1	0	9.8e-01	0.06	2.8e-01	9.9e-01	▁▁▁▁▇
diet_Vitamins_vit_b1_thiamin	2	0	9.8e-01	0.00	9.8e-01	9.8e-01	▁▁▇▁▁
diet_Vitamins_vit_b2_riboflavin	1	0	9.0e-01	0.03	6.8e-01	9.4e-01	▁▁▁▇▅
diet_Vitamins_vit_b2_riboflavin	2	0	9.4e-01	0.00	9.4e-01	9.4e-01	▁▁▇▁▁
diet_Vitamins_vit_b3_niacin	1	0	9.9e-01	0.04	4.9e-01	1.0e+00	▁▁▁▁▇
diet_Vitamins_vit_b3_niacin	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Vitamins_choline	1	0	5.0e-01	0.01	4.6e-01	5.2e-01	▁▁▇▁▅
diet_Vitamins_choline	2	0	5.2e-01	0.00	5.2e-01	5.2e-01	▁▁▇▁▁
diet_Vitamins_vit_b5_pantothenic_acid	1	0	9.2e-01	0.04	3.9e-01	9.4e-01	▁▁▁▁▇
diet_Vitamins_vit_b5_pantothenic_acid	2	0	9.4e-01	0.00	9.4e-01	9.4e-01	▁▁▇▁▁
diet_Vitamins_vit_b6_pyridoxine	1	0	9.6e-01	0.05	3.5e-01	9.6e-01	▁▁▁▁▇
diet_Vitamins_vit_b6_pyridoxine	2	0	9.6e-01	0.00	9.6e-01	9.6e-01	▁▁▇▁▁
diet_Vitamins_vit_b7_biotin	1	0	9.9e-01	0.02	7.2e-01	1.0e+00	▁▁▁▁▇
diet_Vitamins_vit_b7_biotin	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Vitamins_folic_acid	1	0	9.9e-01	0.07	5.0e-02	1.0e+00	▁▁▁▁▇
diet_Vitamins_folic_acid	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Vitamins_vit_b12	1	0	4.9e-01	0.07	4.5e-01	1.0e+00	▇▅▁▁▁
diet_Vitamins_vit_b12	2	0	5.6e-01	0.00	5.6e-01	5.6e-01	▁▁▇▁▁
diet_Vitamins_vit_c	1	0	9.7e-01	0.04	6.5e-01	1.0e+00	▁▁▁▁▇
diet_Vitamins_vit_c	2	0	9.3e-01	0.00	9.3e-01	9.3e-01	▁▁▇▁▁
diet_Vitamins_beta_carotene	1	0	2.1e-01	0.02	1.8e-01	2.4e-01	▅▁▁▇▁
diet_Vitamins_beta_carotene	2	0	1.8e-01	0.00	1.8e-01	1.8e-01	▁▁▇▁▁
diet_Vitamins_lycopene	1	0	4.0e-01	0.06	3.2e-01	6.5e-01	▅▇▁▁▁
diet_Vitamins_lycopene	2	0	3.2e-01	0.00	3.2e-01	3.2e-01	▁▁▇▁▁

Clean up Taxonomy

Prune Taxa

Drop the OTUs with the lowest abundances. Constraining your dataset to taxa with >1 total read helps stabilize diversity estimates and speeds up tree operations.

First let’s see how many OTUs we lose at different thresholds

rare_otus <- names(taxa_sums(phylo_merged)[taxa_sums(phylo_merged) <= 1])
keep_otus <- names(taxa_sums(phylo_merged)[taxa_sums(phylo_merged) > 1])

length(rare_otus)

[1] 599

length(keep_otus)

[1] 1645

We will remove 599 otus where the total abundance is less than or equal to 1 and keep the 1645 otus with a total abundance of at least 2. (You can change the value in the keep_otus function above to use a higher/lower threshold.)

phylo_pruned <- prune_taxa(keep_otus, phylo_merged)
skim_phyloseq(phylo_pruned)

Data summary

Name	Piped data
Number of rows	633325
Number of columns	87
_______________________
Column type frequency:
character	9
numeric	77
________________________
Group variables	subject

Variable type: character

skim_variable	subject	n_missing	min	max	empty	n_unique	whitespace
OTU	1	0	6	11	0	1645	0
OTU	2	0	6	11	0	1645	0
Sample	1	0	6	8	0	166	0
Sample	2	0	8	10	0	219	0
Kingdom	1	0	11	11	0	1	0
Kingdom	2	0	11	11	0	1	0
Phylum	1	0	13	29	0	25	0
Phylum	2	0	13	29	0	25	0
Class	1	0	3	24	0	51	0
Class	2	0	3	24	0	51	0
Order	1	0	3	26	0	105	0
Order	2	0	3	26	0	105	0
Family	1	0	3	32	0	218	0
Family	2	0	3	32	0	218	0
Genus	1	0	3	26	0	699	0
Genus	2	0	3	26	0	699	0
Species	1	0	15	46	0	1645	0
Species	2	0	15	46	0	1645	0

Variable type: numeric

skim_variable	subject	n_missing	mean	sd	p0	p100	hist
Abundance	1	0	4.6e+00	76.55	0.0e+00	7.2e+03	▇▁▁▁▁
Abundance	2	0	3.9e+00	68.54	0.0e+00	8.5e+03	▇▁▁▁▁
subject_sex	1	0	2.0e+00	0.00	2.0e+00	2.0e+00	▁▁▇▁▁
subject_sex	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
subject_day	1	0	8.4e+01	47.92	1.0e+00	1.7e+02	▇▇▇▇▇
subject_day	2	0	2.8e+02	63.22	1.7e+02	3.8e+02	▇▇▇▇▇
collection_day	1	0	1.0e+02	62.09	1.0e+00	4.2e+02	▇▇▂▁▁
collection_day	2	0	1.6e+02	95.94	1.0e+00	3.3e+02	▇▇▇▆▇
collection_date	1	0	2.0e+04	62.09	2.0e+04	2.0e+04	▇▇▂▁▁
collection_date	2	0	2.0e+04	95.94	2.0e+04	2.0e+04	▇▇▇▆▇
environment_holding	1	0	1.6e+00	0.48	1.0e+00	2.0e+00	▅▁▁▁▇
environment_holding	2	0	1.4e+00	0.49	1.0e+00	2.0e+00	▇▁▁▁▆
environment_pair_access	1	0	1.1e+00	0.29	1.0e+00	2.0e+00	▇▁▁▁▁
environment_pair_access	2	0	1.1e+00	0.22	1.0e+00	2.0e+00	▇▁▁▁▁
repro_warb_status	1	0	1.4e+00	0.73	1.0e+00	3.0e+00	▇▁▁▁▂
repro_warb_status	2	0	2.0e+00	0.96	1.0e+00	3.0e+00	▇▁▂▁▇
bristol_min	1	6580	3.1e+00	1.02	0.0e+00	6.0e+00	▁▂▇▁▂
bristol_min	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
bristol_mean	1	0	3.5e+00	1.17	0.0e+00	6.0e+00	▁▃▇▃▂
bristol_mean	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
bristol_max	1	6580	3.8e+00	1.55	0.0e+00	6.0e+00	▁▃▇▁▆
bristol_max	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
supplement_probiotic	1	0	7.1e-01	0.44	0.0e+00	1.0e+00	▃▁▁▁▇
supplement_probiotic	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_steroid	1	0	7.0e-01	0.43	0.0e+00	1.0e+00	▃▁▂▁▇
supplement_steroid	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_fiber	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_fiber	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_antibiotic	1	0	2.0e-02	0.13	0.0e+00	1.0e+00	▇▁▁▁▁
supplement_antibiotic	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_antidiarrheal	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_antidiarrheal	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_name	1	0	1.7e+00	0.49	1.0e+00	3.0e+00	▅▁▇▁▁
diet_name	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Total_total	1	0	6.0e-01	0.04	5.4e-01	7.3e-01	▅▁▇▁▁
diet_Total_total	2	0	5.4e-01	0.00	5.4e-01	5.4e-01	▁▁▇▁▁
diet_Protein_total	1	0	6.7e-01	0.02	6.0e-01	6.9e-01	▁▁▁▇▅
diet_Protein_total	2	0	6.9e-01	0.00	6.9e-01	6.9e-01	▁▁▇▁▁
diet_Fat_total	1	0	6.2e-01	0.01	6.1e-01	7.0e-01	▇▅▁▁▁
diet_Fat_total	2	0	6.3e-01	0.00	6.3e-01	6.3e-01	▁▁▇▁▁
diet_CHO_by_diff_total	1	0	3.4e-01	0.04	2.9e-01	6.0e-01	▅▇▁▁▁
diet_CHO_by_diff_total	2	0	2.9e-01	0.00	2.9e-01	2.9e-01	▁▁▇▁▁
diet_Ash_total	1	0	1.0e+00	0.02	7.9e-01	1.0e+00	▁▁▁▁▇
diet_Ash_total	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Total_dry_matter	1	0	5.6e-01	0.01	5.3e-01	5.6e-01	▁▁▁▅▇
diet_Total_dry_matter	2	0	5.5e-01	0.00	5.5e-01	5.5e-01	▁▁▇▁▁
diet_Total_kcal	1	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_Total_kcal	2	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_Foods_biscuit_rotation	1	0	2.3e-01	0.02	0.0e+00	2.5e-01	▁▁▁▁▇
diet_Foods_biscuit_rotation	2	0	2.5e-01	0.00	2.5e-01	2.5e-01	▁▁▇▁▁
diet_Foods_invertebrate_misc	1	0	1.5e-01	0.03	1.3e-01	2.6e-01	▇▁▅▁▁
diet_Foods_invertebrate_misc	2	0	1.9e-01	0.00	1.9e-01	1.9e-01	▁▁▇▁▁
diet_Foods_mazuri_enrich_gum_arabic_5b35	1	0	2.1e-01	0.01	2.0e-01	2.4e-01	▇▁▁▅▁
diet_Foods_mazuri_enrich_gum_arabic_5b35	2	0	2.3e-01	0.00	2.3e-01	2.3e-01	▁▁▇▁▁
diet_Foods_protein_rotation	1	0	4.0e-02	0.00	4.0e-02	7.0e-02	▇▁▁▁▁
diet_Foods_protein_rotation	2	0	4.0e-02	0.00	4.0e-02	4.0e-02	▁▁▇▁▁
diet_Foods_seasonal_vegetables	1	0	3.2e-01	0.02	2.9e-01	4.3e-01	▅▇▁▁▁
diet_Foods_seasonal_vegetables	2	0	2.9e-01	0.00	2.9e-01	2.9e-01	▁▁▇▁▁
diet_Foods_egg_whole_cooked_meat_prep	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_whole_cooked_meat_prep	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_bean_green_fresh	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_bean_green_fresh	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_carrot	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_carrot	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_pumpkin_canned	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_pumpkin_canned	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_whole_raw_meat_prep	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_whole_raw_meat_prep	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_root	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_root	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_non_root	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_non_root	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_white_only_cooked	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_white_only_cooked	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet_cooked	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet_cooked	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_celery	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_celery	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_lettuce_romaine	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_lettuce_romaine	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_ohdz_oatmeal_gel	1	0	5.0e-02	0.04	0.0e+00	8.0e-02	▅▁▁▁▇
diet_Foods_ohdz_oatmeal_gel	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Protein_taurine	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Protein_taurine	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Protein_methionine	1	0	4.4e-01	0.01	3.2e-01	4.5e-01	▁▁▁▁▇
diet_Protein_methionine	2	0	4.5e-01	0.00	4.5e-01	4.5e-01	▁▁▇▁▁
diet_Fat_omega_3_ala__epa__dha	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Fat_omega_3_ala__epa__dha	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Fat_omega_6_la__gla__aa	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Fat_omega_6_la__gla__aa	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_CHO_by_diff_starch	1	0	9.5e-01	0.07	3.8e-01	1.0e+00	▁▁▁▁▇
diet_CHO_by_diff_starch	2	0	8.8e-01	0.00	8.8e-01	8.8e-01	▁▁▇▁▁
diet_CHO_by_diff_ndf	1	0	7.8e-01	0.01	7.5e-01	7.8e-01	▁▁▅▁▇
diet_CHO_by_diff_ndf	2	0	7.7e-01	0.00	7.7e-01	7.7e-01	▁▁▇▁▁
diet_CHO_by_diff_adf	1	0	9.9e-01	0.05	3.1e-01	1.0e+00	▁▁▁▁▇
diet_CHO_by_diff_adf	2	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_CHO_by_diff_crude_fiber	1	0	5.7e-01	0.01	5.7e-01	7.1e-01	▇▁▁▁▁
diet_CHO_by_diff_crude_fiber	2	0	5.7e-01	0.00	5.7e-01	5.7e-01	▁▁▇▁▁
diet_CHO_by_diff_tdf	1	0	4.1e-01	0.04	3.8e-01	7.6e-01	▇▁▁▁▁
diet_CHO_by_diff_tdf	2	0	3.8e-01	0.00	3.8e-01	3.8e-01	▁▁▇▁▁
diet_Ash_calcium	1	0	9.6e-01	0.01	7.7e-01	9.7e-01	▁▁▁▁▇
diet_Ash_calcium	2	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_Ash_phosphorus	1	0	9.6e-01	0.03	9.4e-01	1.0e+00	▇▁▁▁▅
diet_Ash_phosphorus	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_potassium	1	0	8.8e-01	0.00	8.8e-01	9.1e-01	▇▁▁▁▁
diet_Ash_potassium	2	0	8.8e-01	0.00	8.8e-01	8.8e-01	▁▁▇▁▁
diet_Ash_magnesium	1	0	5.0e-01	0.05	4.4e-01	7.2e-01	▅▇▁▁▁
diet_Ash_magnesium	2	0	4.4e-01	0.00	4.4e-01	4.4e-01	▁▁▇▁▁
diet_Ash_copper	1	0	9.9e-01	0.05	3.1e-01	1.0e+00	▁▁▁▁▇
diet_Ash_copper	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_iron	1	0	9.8e-01	0.02	7.9e-01	1.0e+00	▁▁▁▁▇
diet_Ash_iron	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_zinc	1	0	9.8e-01	0.04	4.7e-01	1.0e+00	▁▁▁▁▇
diet_Ash_zinc	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_manganese	1	0	9.9e-01	0.06	2.4e-01	1.0e+00	▁▁▁▁▇
diet_Ash_manganese	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_iodine	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Ash_iodine	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_ca_p	1	0	8.4e-01	0.02	6.9e-01	8.6e-01	▁▁▁▅▇
diet_Ash_ca_p	2	0	8.1e-01	0.00	8.1e-01	8.1e-01	▁▁▇▁▁
diet_Vitamins_vit_a_iu	1	0	2.5e-01	0.02	2.3e-01	2.7e-01	▅▁▁▁▇
diet_Vitamins_vit_a_iu	2	0	2.3e-01	0.00	2.3e-01	2.3e-01	▁▁▇▁▁
diet_Vitamins_vit_d3	1	0	9.8e-01	0.02	7.3e-01	9.8e-01	▁▁▁▁▇
diet_Vitamins_vit_d3	2	0	9.8e-01	0.00	9.8e-01	9.8e-01	▁▁▇▁▁
diet_Vitamins_vit_e	1	0	9.3e-01	0.10	6.4e-01	1.0e+00	▁▁▅▁▇
diet_Vitamins_vit_e	2	0	8.0e-01	0.00	8.0e-01	8.0e-01	▁▁▇▁▁
diet_Vitamins_vit_k	1	0	9.7e-01	0.04	4.0e-01	9.8e-01	▁▁▁▁▇
diet_Vitamins_vit_k	2	0	9.6e-01	0.00	9.6e-01	9.6e-01	▁▁▇▁▁
diet_Vitamins_vit_b1_thiamin	1	0	9.8e-01	0.06	2.8e-01	9.9e-01	▁▁▁▁▇
diet_Vitamins_vit_b1_thiamin	2	0	9.8e-01	0.00	9.8e-01	9.8e-01	▁▁▇▁▁
diet_Vitamins_vit_b2_riboflavin	1	0	9.0e-01	0.03	6.8e-01	9.4e-01	▁▁▁▇▅
diet_Vitamins_vit_b2_riboflavin	2	0	9.4e-01	0.00	9.4e-01	9.4e-01	▁▁▇▁▁
diet_Vitamins_vit_b3_niacin	1	0	9.9e-01	0.04	4.9e-01	1.0e+00	▁▁▁▁▇
diet_Vitamins_vit_b3_niacin	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Vitamins_choline	1	0	5.0e-01	0.01	4.6e-01	5.2e-01	▁▁▇▁▅
diet_Vitamins_choline	2	0	5.2e-01	0.00	5.2e-01	5.2e-01	▁▁▇▁▁
diet_Vitamins_vit_b5_pantothenic_acid	1	0	9.2e-01	0.04	3.9e-01	9.4e-01	▁▁▁▁▇
diet_Vitamins_vit_b5_pantothenic_acid	2	0	9.4e-01	0.00	9.4e-01	9.4e-01	▁▁▇▁▁
diet_Vitamins_vit_b6_pyridoxine	1	0	9.6e-01	0.05	3.5e-01	9.6e-01	▁▁▁▁▇
diet_Vitamins_vit_b6_pyridoxine	2	0	9.6e-01	0.00	9.6e-01	9.6e-01	▁▁▇▁▁
diet_Vitamins_vit_b7_biotin	1	0	9.9e-01	0.02	7.2e-01	1.0e+00	▁▁▁▁▇
diet_Vitamins_vit_b7_biotin	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Vitamins_folic_acid	1	0	9.9e-01	0.07	5.0e-02	1.0e+00	▁▁▁▁▇
diet_Vitamins_folic_acid	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Vitamins_vit_b12	1	0	4.9e-01	0.07	4.5e-01	1.0e+00	▇▅▁▁▁
diet_Vitamins_vit_b12	2	0	5.6e-01	0.00	5.6e-01	5.6e-01	▁▁▇▁▁
diet_Vitamins_vit_c	1	0	9.7e-01	0.04	6.5e-01	1.0e+00	▁▁▁▁▇
diet_Vitamins_vit_c	2	0	9.3e-01	0.00	9.3e-01	9.3e-01	▁▁▇▁▁
diet_Vitamins_beta_carotene	1	0	2.1e-01	0.02	1.8e-01	2.4e-01	▅▁▁▇▁
diet_Vitamins_beta_carotene	2	0	1.8e-01	0.00	1.8e-01	1.8e-01	▁▁▇▁▁
diet_Vitamins_lycopene	1	0	4.0e-01	0.06	3.2e-01	6.5e-01	▅▇▁▁▁
diet_Vitamins_lycopene	2	0	3.2e-01	0.00	3.2e-01	3.2e-01	▁▁▇▁▁

view_tree(phylo_pruned) + ggtitle("Species-Level OTUs after Filtering")

Collapse Taxa to Genus Level

Collapsing at genus level reduces complexity and allows genus‐scale ecological patterns to emerge with less interference from random error at the species-assignment level.

phylo_genus <- phylo_pruned %>% tax_glom("Genus")
skim_phyloseq(phylo_genus)

Data summary

Name	Piped data
Number of rows	270270
Number of columns	86
_______________________
Column type frequency:
character	8
numeric	77
________________________
Group variables	subject

Variable type: character

skim_variable	subject	n_missing	min	max	empty	n_unique	whitespace
OTU	1	0	6	11	0	702	0
OTU	2	0	6	11	0	702	0
Sample	1	0	6	8	0	166	0
Sample	2	0	8	10	0	219	0
Kingdom	1	0	11	11	0	1	0
Kingdom	2	0	11	11	0	1	0
Phylum	1	0	13	29	0	25	0
Phylum	2	0	13	29	0	25	0
Class	1	0	3	24	0	51	0
Class	2	0	3	24	0	51	0
Order	1	0	3	26	0	105	0
Order	2	0	3	26	0	105	0
Family	1	0	3	32	0	218	0
Family	2	0	3	32	0	218	0
Genus	1	0	3	26	0	699	0
Genus	2	0	3	26	0	699	0

Variable type: numeric

skim_variable	subject	n_missing	mean	sd	p0	p100	hist
Abundance	1	0	1.1e+01	135.14	0.0e+00	7.8e+03	▇▁▁▁▁
Abundance	2	0	9.1e+00	119.98	0.0e+00	9.2e+03	▇▁▁▁▁
subject_sex	1	0	2.0e+00	0.00	2.0e+00	2.0e+00	▁▁▇▁▁
subject_sex	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
subject_day	1	0	8.4e+01	47.92	1.0e+00	1.7e+02	▇▇▇▇▇
subject_day	2	0	2.8e+02	63.22	1.7e+02	3.8e+02	▇▇▇▇▇
collection_day	1	0	1.0e+02	62.09	1.0e+00	4.2e+02	▇▇▂▁▁
collection_day	2	0	1.6e+02	95.94	1.0e+00	3.3e+02	▇▇▇▆▇
collection_date	1	0	2.0e+04	62.09	2.0e+04	2.0e+04	▇▇▂▁▁
collection_date	2	0	2.0e+04	95.94	2.0e+04	2.0e+04	▇▇▇▆▇
environment_holding	1	0	1.6e+00	0.48	1.0e+00	2.0e+00	▅▁▁▁▇
environment_holding	2	0	1.4e+00	0.49	1.0e+00	2.0e+00	▇▁▁▁▆
environment_pair_access	1	0	1.1e+00	0.29	1.0e+00	2.0e+00	▇▁▁▁▁
environment_pair_access	2	0	1.1e+00	0.22	1.0e+00	2.0e+00	▇▁▁▁▁
repro_warb_status	1	0	1.4e+00	0.73	1.0e+00	3.0e+00	▇▁▁▁▂
repro_warb_status	2	0	2.0e+00	0.96	1.0e+00	3.0e+00	▇▁▂▁▇
bristol_min	1	2808	3.1e+00	1.02	0.0e+00	6.0e+00	▁▂▇▁▂
bristol_min	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
bristol_mean	1	0	3.5e+00	1.17	0.0e+00	6.0e+00	▁▃▇▃▂
bristol_mean	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
bristol_max	1	2808	3.8e+00	1.55	0.0e+00	6.0e+00	▁▃▇▁▆
bristol_max	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
supplement_probiotic	1	0	7.1e-01	0.44	0.0e+00	1.0e+00	▃▁▁▁▇
supplement_probiotic	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_steroid	1	0	7.0e-01	0.43	0.0e+00	1.0e+00	▃▁▂▁▇
supplement_steroid	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_fiber	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_fiber	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_antibiotic	1	0	2.0e-02	0.13	0.0e+00	1.0e+00	▇▁▁▁▁
supplement_antibiotic	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_antidiarrheal	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_antidiarrheal	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_name	1	0	1.7e+00	0.49	1.0e+00	3.0e+00	▅▁▇▁▁
diet_name	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Total_total	1	0	6.0e-01	0.04	5.4e-01	7.3e-01	▅▁▇▁▁
diet_Total_total	2	0	5.4e-01	0.00	5.4e-01	5.4e-01	▁▁▇▁▁
diet_Protein_total	1	0	6.7e-01	0.02	6.0e-01	6.9e-01	▁▁▁▇▅
diet_Protein_total	2	0	6.9e-01	0.00	6.9e-01	6.9e-01	▁▁▇▁▁
diet_Fat_total	1	0	6.2e-01	0.01	6.1e-01	7.0e-01	▇▅▁▁▁
diet_Fat_total	2	0	6.3e-01	0.00	6.3e-01	6.3e-01	▁▁▇▁▁
diet_CHO_by_diff_total	1	0	3.4e-01	0.04	2.9e-01	6.0e-01	▅▇▁▁▁
diet_CHO_by_diff_total	2	0	2.9e-01	0.00	2.9e-01	2.9e-01	▁▁▇▁▁
diet_Ash_total	1	0	1.0e+00	0.02	7.9e-01	1.0e+00	▁▁▁▁▇
diet_Ash_total	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Total_dry_matter	1	0	5.6e-01	0.01	5.3e-01	5.6e-01	▁▁▁▅▇
diet_Total_dry_matter	2	0	5.5e-01	0.00	5.5e-01	5.5e-01	▁▁▇▁▁
diet_Total_kcal	1	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_Total_kcal	2	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_Foods_biscuit_rotation	1	0	2.3e-01	0.02	0.0e+00	2.5e-01	▁▁▁▁▇
diet_Foods_biscuit_rotation	2	0	2.5e-01	0.00	2.5e-01	2.5e-01	▁▁▇▁▁
diet_Foods_invertebrate_misc	1	0	1.5e-01	0.03	1.3e-01	2.6e-01	▇▁▅▁▁
diet_Foods_invertebrate_misc	2	0	1.9e-01	0.00	1.9e-01	1.9e-01	▁▁▇▁▁
diet_Foods_mazuri_enrich_gum_arabic_5b35	1	0	2.1e-01	0.01	2.0e-01	2.4e-01	▇▁▁▅▁
diet_Foods_mazuri_enrich_gum_arabic_5b35	2	0	2.3e-01	0.00	2.3e-01	2.3e-01	▁▁▇▁▁
diet_Foods_protein_rotation	1	0	4.0e-02	0.00	4.0e-02	7.0e-02	▇▁▁▁▁
diet_Foods_protein_rotation	2	0	4.0e-02	0.00	4.0e-02	4.0e-02	▁▁▇▁▁
diet_Foods_seasonal_vegetables	1	0	3.2e-01	0.02	2.9e-01	4.3e-01	▅▇▁▁▁
diet_Foods_seasonal_vegetables	2	0	2.9e-01	0.00	2.9e-01	2.9e-01	▁▁▇▁▁
diet_Foods_egg_whole_cooked_meat_prep	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_whole_cooked_meat_prep	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_bean_green_fresh	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_bean_green_fresh	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_carrot	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_carrot	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_pumpkin_canned	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_pumpkin_canned	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_whole_raw_meat_prep	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_whole_raw_meat_prep	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_root	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_root	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_non_root	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_non_root	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_white_only_cooked	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_white_only_cooked	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet_cooked	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet_cooked	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_celery	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_celery	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_lettuce_romaine	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_lettuce_romaine	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_ohdz_oatmeal_gel	1	0	5.0e-02	0.04	0.0e+00	8.0e-02	▅▁▁▁▇
diet_Foods_ohdz_oatmeal_gel	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Protein_taurine	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Protein_taurine	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Protein_methionine	1	0	4.4e-01	0.01	3.2e-01	4.5e-01	▁▁▁▁▇
diet_Protein_methionine	2	0	4.5e-01	0.00	4.5e-01	4.5e-01	▁▁▇▁▁
diet_Fat_omega_3_ala__epa__dha	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Fat_omega_3_ala__epa__dha	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Fat_omega_6_la__gla__aa	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Fat_omega_6_la__gla__aa	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_CHO_by_diff_starch	1	0	9.5e-01	0.07	3.8e-01	1.0e+00	▁▁▁▁▇
diet_CHO_by_diff_starch	2	0	8.8e-01	0.00	8.8e-01	8.8e-01	▁▁▇▁▁
diet_CHO_by_diff_ndf	1	0	7.8e-01	0.01	7.5e-01	7.8e-01	▁▁▅▁▇
diet_CHO_by_diff_ndf	2	0	7.7e-01	0.00	7.7e-01	7.7e-01	▁▁▇▁▁
diet_CHO_by_diff_adf	1	0	9.9e-01	0.05	3.1e-01	1.0e+00	▁▁▁▁▇
diet_CHO_by_diff_adf	2	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_CHO_by_diff_crude_fiber	1	0	5.7e-01	0.01	5.7e-01	7.1e-01	▇▁▁▁▁
diet_CHO_by_diff_crude_fiber	2	0	5.7e-01	0.00	5.7e-01	5.7e-01	▁▁▇▁▁
diet_CHO_by_diff_tdf	1	0	4.1e-01	0.04	3.8e-01	7.6e-01	▇▁▁▁▁
diet_CHO_by_diff_tdf	2	0	3.8e-01	0.00	3.8e-01	3.8e-01	▁▁▇▁▁
diet_Ash_calcium	1	0	9.6e-01	0.01	7.7e-01	9.7e-01	▁▁▁▁▇
diet_Ash_calcium	2	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_Ash_phosphorus	1	0	9.6e-01	0.03	9.4e-01	1.0e+00	▇▁▁▁▅
diet_Ash_phosphorus	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_potassium	1	0	8.8e-01	0.00	8.8e-01	9.1e-01	▇▁▁▁▁
diet_Ash_potassium	2	0	8.8e-01	0.00	8.8e-01	8.8e-01	▁▁▇▁▁
diet_Ash_magnesium	1	0	5.0e-01	0.05	4.4e-01	7.2e-01	▅▇▁▁▁
diet_Ash_magnesium	2	0	4.4e-01	0.00	4.4e-01	4.4e-01	▁▁▇▁▁
diet_Ash_copper	1	0	9.9e-01	0.05	3.1e-01	1.0e+00	▁▁▁▁▇
diet_Ash_copper	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_iron	1	0	9.8e-01	0.02	7.9e-01	1.0e+00	▁▁▁▁▇
diet_Ash_iron	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_zinc	1	0	9.8e-01	0.04	4.7e-01	1.0e+00	▁▁▁▁▇
diet_Ash_zinc	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_manganese	1	0	9.9e-01	0.06	2.4e-01	1.0e+00	▁▁▁▁▇
diet_Ash_manganese	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_iodine	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Ash_iodine	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_ca_p	1	0	8.4e-01	0.02	6.9e-01	8.6e-01	▁▁▁▅▇
diet_Ash_ca_p	2	0	8.1e-01	0.00	8.1e-01	8.1e-01	▁▁▇▁▁
diet_Vitamins_vit_a_iu	1	0	2.5e-01	0.02	2.3e-01	2.7e-01	▅▁▁▁▇
diet_Vitamins_vit_a_iu	2	0	2.3e-01	0.00	2.3e-01	2.3e-01	▁▁▇▁▁
diet_Vitamins_vit_d3	1	0	9.8e-01	0.02	7.3e-01	9.8e-01	▁▁▁▁▇
diet_Vitamins_vit_d3	2	0	9.8e-01	0.00	9.8e-01	9.8e-01	▁▁▇▁▁
diet_Vitamins_vit_e	1	0	9.3e-01	0.10	6.4e-01	1.0e+00	▁▁▅▁▇
diet_Vitamins_vit_e	2	0	8.0e-01	0.00	8.0e-01	8.0e-01	▁▁▇▁▁
diet_Vitamins_vit_k	1	0	9.7e-01	0.04	4.0e-01	9.8e-01	▁▁▁▁▇
diet_Vitamins_vit_k	2	0	9.6e-01	0.00	9.6e-01	9.6e-01	▁▁▇▁▁
diet_Vitamins_vit_b1_thiamin	1	0	9.8e-01	0.06	2.8e-01	9.9e-01	▁▁▁▁▇
diet_Vitamins_vit_b1_thiamin	2	0	9.8e-01	0.00	9.8e-01	9.8e-01	▁▁▇▁▁
diet_Vitamins_vit_b2_riboflavin	1	0	9.0e-01	0.03	6.8e-01	9.4e-01	▁▁▁▇▅
diet_Vitamins_vit_b2_riboflavin	2	0	9.4e-01	0.00	9.4e-01	9.4e-01	▁▁▇▁▁
diet_Vitamins_vit_b3_niacin	1	0	9.9e-01	0.04	4.9e-01	1.0e+00	▁▁▁▁▇
diet_Vitamins_vit_b3_niacin	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Vitamins_choline	1	0	5.0e-01	0.01	4.6e-01	5.2e-01	▁▁▇▁▅
diet_Vitamins_choline	2	0	5.2e-01	0.00	5.2e-01	5.2e-01	▁▁▇▁▁
diet_Vitamins_vit_b5_pantothenic_acid	1	0	9.2e-01	0.04	3.9e-01	9.4e-01	▁▁▁▁▇
diet_Vitamins_vit_b5_pantothenic_acid	2	0	9.4e-01	0.00	9.4e-01	9.4e-01	▁▁▇▁▁
diet_Vitamins_vit_b6_pyridoxine	1	0	9.6e-01	0.05	3.5e-01	9.6e-01	▁▁▁▁▇
diet_Vitamins_vit_b6_pyridoxine	2	0	9.6e-01	0.00	9.6e-01	9.6e-01	▁▁▇▁▁
diet_Vitamins_vit_b7_biotin	1	0	9.9e-01	0.02	7.2e-01	1.0e+00	▁▁▁▁▇
diet_Vitamins_vit_b7_biotin	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Vitamins_folic_acid	1	0	9.9e-01	0.07	5.0e-02	1.0e+00	▁▁▁▁▇
diet_Vitamins_folic_acid	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Vitamins_vit_b12	1	0	4.9e-01	0.07	4.5e-01	1.0e+00	▇▅▁▁▁
diet_Vitamins_vit_b12	2	0	5.6e-01	0.00	5.6e-01	5.6e-01	▁▁▇▁▁
diet_Vitamins_vit_c	1	0	9.7e-01	0.04	6.5e-01	1.0e+00	▁▁▁▁▇
diet_Vitamins_vit_c	2	0	9.3e-01	0.00	9.3e-01	9.3e-01	▁▁▇▁▁
diet_Vitamins_beta_carotene	1	0	2.1e-01	0.02	1.8e-01	2.4e-01	▅▁▁▇▁
diet_Vitamins_beta_carotene	2	0	1.8e-01	0.00	1.8e-01	1.8e-01	▁▁▇▁▁
diet_Vitamins_lycopene	1	0	4.0e-01	0.06	3.2e-01	6.5e-01	▅▇▁▁▁
diet_Vitamins_lycopene	2	0	3.2e-01	0.00	3.2e-01	3.2e-01	▁▁▇▁▁

view_tree(phylo_genus) + ggtitle("Genus-Level OTUs")

Another Pruning Check

Let’s look at our OTUs now that we merged to the Genus level

rare_genera <- names(taxa_sums(phylo_genus)[taxa_sums(phylo_genus) <= 2])
keep_genera <- names(taxa_sums(phylo_genus)[taxa_sums(phylo_genus) > 2])
length(rare_genera)

[1] 89

length(keep_genera)

[1] 613

Let’s do one more pruning step to only keep the genera with total abundances greater than 2 (613 genera total).

phylo_clean <- prune_taxa(keep_genera, phylo_genus)
skim_phyloseq(phylo_clean)

Data summary

Name	Piped data
Number of rows	236005
Number of columns	86
_______________________
Column type frequency:
character	8
numeric	77
________________________
Group variables	subject

Variable type: character

skim_variable	subject	n_missing	min	max	empty	n_unique	whitespace
OTU	1	0	6	11	0	613	0
OTU	2	0	6	11	0	613	0
Sample	1	0	6	8	0	166	0
Sample	2	0	8	10	0	219	0
Kingdom	1	0	11	11	0	1	0
Kingdom	2	0	11	11	0	1	0
Phylum	1	0	13	29	0	23	0
Phylum	2	0	13	29	0	23	0
Class	1	0	3	24	0	50	0
Class	2	0	3	24	0	50	0
Order	1	0	3	26	0	98	0
Order	2	0	3	26	0	98	0
Family	1	0	3	32	0	198	0
Family	2	0	3	32	0	198	0
Genus	1	0	3	25	0	611	0
Genus	2	0	3	25	0	611	0

Variable type: numeric

skim_variable	subject	n_missing	mean	sd	p0	p100	hist
Abundance	1	0	1.2e+01	144.55	0.0e+00	7.8e+03	▇▁▁▁▁
Abundance	2	0	1.0e+01	128.35	0.0e+00	9.2e+03	▇▁▁▁▁
subject_sex	1	0	2.0e+00	0.00	2.0e+00	2.0e+00	▁▁▇▁▁
subject_sex	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
subject_day	1	0	8.4e+01	47.92	1.0e+00	1.7e+02	▇▇▇▇▇
subject_day	2	0	2.8e+02	63.22	1.7e+02	3.8e+02	▇▇▇▇▇
collection_day	1	0	1.0e+02	62.09	1.0e+00	4.2e+02	▇▇▂▁▁
collection_day	2	0	1.6e+02	95.94	1.0e+00	3.3e+02	▇▇▇▆▇
collection_date	1	0	2.0e+04	62.09	2.0e+04	2.0e+04	▇▇▂▁▁
collection_date	2	0	2.0e+04	95.94	2.0e+04	2.0e+04	▇▇▇▆▇
environment_holding	1	0	1.6e+00	0.48	1.0e+00	2.0e+00	▅▁▁▁▇
environment_holding	2	0	1.4e+00	0.49	1.0e+00	2.0e+00	▇▁▁▁▆
environment_pair_access	1	0	1.1e+00	0.29	1.0e+00	2.0e+00	▇▁▁▁▁
environment_pair_access	2	0	1.1e+00	0.22	1.0e+00	2.0e+00	▇▁▁▁▁
repro_warb_status	1	0	1.4e+00	0.73	1.0e+00	3.0e+00	▇▁▁▁▂
repro_warb_status	2	0	2.0e+00	0.96	1.0e+00	3.0e+00	▇▁▂▁▇
bristol_min	1	2452	3.1e+00	1.02	0.0e+00	6.0e+00	▁▂▇▁▂
bristol_min	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
bristol_mean	1	0	3.5e+00	1.17	0.0e+00	6.0e+00	▁▃▇▃▂
bristol_mean	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
bristol_max	1	2452	3.8e+00	1.55	0.0e+00	6.0e+00	▁▃▇▁▆
bristol_max	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
supplement_probiotic	1	0	7.1e-01	0.44	0.0e+00	1.0e+00	▃▁▁▁▇
supplement_probiotic	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_steroid	1	0	7.0e-01	0.43	0.0e+00	1.0e+00	▃▁▂▁▇
supplement_steroid	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_fiber	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_fiber	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_antibiotic	1	0	2.0e-02	0.13	0.0e+00	1.0e+00	▇▁▁▁▁
supplement_antibiotic	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_antidiarrheal	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
supplement_antidiarrheal	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_name	1	0	1.7e+00	0.49	1.0e+00	3.0e+00	▅▁▇▁▁
diet_name	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Total_total	1	0	6.0e-01	0.04	5.4e-01	7.3e-01	▅▁▇▁▁
diet_Total_total	2	0	5.4e-01	0.00	5.4e-01	5.4e-01	▁▁▇▁▁
diet_Protein_total	1	0	6.7e-01	0.02	6.0e-01	6.9e-01	▁▁▁▇▅
diet_Protein_total	2	0	6.9e-01	0.00	6.9e-01	6.9e-01	▁▁▇▁▁
diet_Fat_total	1	0	6.2e-01	0.01	6.1e-01	7.0e-01	▇▅▁▁▁
diet_Fat_total	2	0	6.3e-01	0.00	6.3e-01	6.3e-01	▁▁▇▁▁
diet_CHO_by_diff_total	1	0	3.4e-01	0.04	2.9e-01	6.0e-01	▅▇▁▁▁
diet_CHO_by_diff_total	2	0	2.9e-01	0.00	2.9e-01	2.9e-01	▁▁▇▁▁
diet_Ash_total	1	0	1.0e+00	0.02	7.9e-01	1.0e+00	▁▁▁▁▇
diet_Ash_total	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Total_dry_matter	1	0	5.6e-01	0.01	5.3e-01	5.6e-01	▁▁▁▅▇
diet_Total_dry_matter	2	0	5.5e-01	0.00	5.5e-01	5.5e-01	▁▁▇▁▁
diet_Total_kcal	1	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_Total_kcal	2	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_Foods_biscuit_rotation	1	0	2.3e-01	0.02	0.0e+00	2.5e-01	▁▁▁▁▇
diet_Foods_biscuit_rotation	2	0	2.5e-01	0.00	2.5e-01	2.5e-01	▁▁▇▁▁
diet_Foods_invertebrate_misc	1	0	1.5e-01	0.03	1.3e-01	2.6e-01	▇▁▅▁▁
diet_Foods_invertebrate_misc	2	0	1.9e-01	0.00	1.9e-01	1.9e-01	▁▁▇▁▁
diet_Foods_mazuri_enrich_gum_arabic_5b35	1	0	2.1e-01	0.01	2.0e-01	2.4e-01	▇▁▁▅▁
diet_Foods_mazuri_enrich_gum_arabic_5b35	2	0	2.3e-01	0.00	2.3e-01	2.3e-01	▁▁▇▁▁
diet_Foods_protein_rotation	1	0	4.0e-02	0.00	4.0e-02	7.0e-02	▇▁▁▁▁
diet_Foods_protein_rotation	2	0	4.0e-02	0.00	4.0e-02	4.0e-02	▁▁▇▁▁
diet_Foods_seasonal_vegetables	1	0	3.2e-01	0.02	2.9e-01	4.3e-01	▅▇▁▁▁
diet_Foods_seasonal_vegetables	2	0	2.9e-01	0.00	2.9e-01	2.9e-01	▁▁▇▁▁
diet_Foods_egg_whole_cooked_meat_prep	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_whole_cooked_meat_prep	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_bean_green_fresh	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_bean_green_fresh	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_carrot	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_carrot	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_pumpkin_canned	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_pumpkin_canned	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_whole_raw_meat_prep	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_whole_raw_meat_prep	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_root	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_root	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_non_root	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_seasonal_vegetables_non_root	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_white_only_cooked	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_egg_white_only_cooked	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet_cooked	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_potato_sweet_cooked	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_celery	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_celery	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_lettuce_romaine	1	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_lettuce_romaine	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Foods_ohdz_oatmeal_gel	1	0	5.0e-02	0.04	0.0e+00	8.0e-02	▅▁▁▁▇
diet_Foods_ohdz_oatmeal_gel	2	0	0.0e+00	0.00	0.0e+00	0.0e+00	▁▁▇▁▁
diet_Protein_taurine	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Protein_taurine	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Protein_methionine	1	0	4.4e-01	0.01	3.2e-01	4.5e-01	▁▁▁▁▇
diet_Protein_methionine	2	0	4.5e-01	0.00	4.5e-01	4.5e-01	▁▁▇▁▁
diet_Fat_omega_3_ala__epa__dha	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Fat_omega_3_ala__epa__dha	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Fat_omega_6_la__gla__aa	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Fat_omega_6_la__gla__aa	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_CHO_by_diff_starch	1	0	9.5e-01	0.07	3.8e-01	1.0e+00	▁▁▁▁▇
diet_CHO_by_diff_starch	2	0	8.8e-01	0.00	8.8e-01	8.8e-01	▁▁▇▁▁
diet_CHO_by_diff_ndf	1	0	7.8e-01	0.01	7.5e-01	7.8e-01	▁▁▅▁▇
diet_CHO_by_diff_ndf	2	0	7.7e-01	0.00	7.7e-01	7.7e-01	▁▁▇▁▁
diet_CHO_by_diff_adf	1	0	9.9e-01	0.05	3.1e-01	1.0e+00	▁▁▁▁▇
diet_CHO_by_diff_adf	2	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_CHO_by_diff_crude_fiber	1	0	5.7e-01	0.01	5.7e-01	7.1e-01	▇▁▁▁▁
diet_CHO_by_diff_crude_fiber	2	0	5.7e-01	0.00	5.7e-01	5.7e-01	▁▁▇▁▁
diet_CHO_by_diff_tdf	1	0	4.1e-01	0.04	3.8e-01	7.6e-01	▇▁▁▁▁
diet_CHO_by_diff_tdf	2	0	3.8e-01	0.00	3.8e-01	3.8e-01	▁▁▇▁▁
diet_Ash_calcium	1	0	9.6e-01	0.01	7.7e-01	9.7e-01	▁▁▁▁▇
diet_Ash_calcium	2	0	9.7e-01	0.00	9.7e-01	9.7e-01	▁▁▇▁▁
diet_Ash_phosphorus	1	0	9.6e-01	0.03	9.4e-01	1.0e+00	▇▁▁▁▅
diet_Ash_phosphorus	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_potassium	1	0	8.8e-01	0.00	8.8e-01	9.1e-01	▇▁▁▁▁
diet_Ash_potassium	2	0	8.8e-01	0.00	8.8e-01	8.8e-01	▁▁▇▁▁
diet_Ash_magnesium	1	0	5.0e-01	0.05	4.4e-01	7.2e-01	▅▇▁▁▁
diet_Ash_magnesium	2	0	4.4e-01	0.00	4.4e-01	4.4e-01	▁▁▇▁▁
diet_Ash_copper	1	0	9.9e-01	0.05	3.1e-01	1.0e+00	▁▁▁▁▇
diet_Ash_copper	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_iron	1	0	9.8e-01	0.02	7.9e-01	1.0e+00	▁▁▁▁▇
diet_Ash_iron	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_zinc	1	0	9.8e-01	0.04	4.7e-01	1.0e+00	▁▁▁▁▇
diet_Ash_zinc	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_manganese	1	0	9.9e-01	0.06	2.4e-01	1.0e+00	▁▁▁▁▇
diet_Ash_manganese	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_iodine	1	0	9.9e-01	0.08	0.0e+00	1.0e+00	▁▁▁▁▇
diet_Ash_iodine	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Ash_ca_p	1	0	8.4e-01	0.02	6.9e-01	8.6e-01	▁▁▁▅▇
diet_Ash_ca_p	2	0	8.1e-01	0.00	8.1e-01	8.1e-01	▁▁▇▁▁
diet_Vitamins_vit_a_iu	1	0	2.5e-01	0.02	2.3e-01	2.7e-01	▅▁▁▁▇
diet_Vitamins_vit_a_iu	2	0	2.3e-01	0.00	2.3e-01	2.3e-01	▁▁▇▁▁
diet_Vitamins_vit_d3	1	0	9.8e-01	0.02	7.3e-01	9.8e-01	▁▁▁▁▇
diet_Vitamins_vit_d3	2	0	9.8e-01	0.00	9.8e-01	9.8e-01	▁▁▇▁▁
diet_Vitamins_vit_e	1	0	9.3e-01	0.10	6.4e-01	1.0e+00	▁▁▅▁▇
diet_Vitamins_vit_e	2	0	8.0e-01	0.00	8.0e-01	8.0e-01	▁▁▇▁▁
diet_Vitamins_vit_k	1	0	9.7e-01	0.04	4.0e-01	9.8e-01	▁▁▁▁▇
diet_Vitamins_vit_k	2	0	9.6e-01	0.00	9.6e-01	9.6e-01	▁▁▇▁▁
diet_Vitamins_vit_b1_thiamin	1	0	9.8e-01	0.06	2.8e-01	9.9e-01	▁▁▁▁▇
diet_Vitamins_vit_b1_thiamin	2	0	9.8e-01	0.00	9.8e-01	9.8e-01	▁▁▇▁▁
diet_Vitamins_vit_b2_riboflavin	1	0	9.0e-01	0.03	6.8e-01	9.4e-01	▁▁▁▇▅
diet_Vitamins_vit_b2_riboflavin	2	0	9.4e-01	0.00	9.4e-01	9.4e-01	▁▁▇▁▁
diet_Vitamins_vit_b3_niacin	1	0	9.9e-01	0.04	4.9e-01	1.0e+00	▁▁▁▁▇
diet_Vitamins_vit_b3_niacin	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Vitamins_choline	1	0	5.0e-01	0.01	4.6e-01	5.2e-01	▁▁▇▁▅
diet_Vitamins_choline	2	0	5.2e-01	0.00	5.2e-01	5.2e-01	▁▁▇▁▁
diet_Vitamins_vit_b5_pantothenic_acid	1	0	9.2e-01	0.04	3.9e-01	9.4e-01	▁▁▁▁▇
diet_Vitamins_vit_b5_pantothenic_acid	2	0	9.4e-01	0.00	9.4e-01	9.4e-01	▁▁▇▁▁
diet_Vitamins_vit_b6_pyridoxine	1	0	9.6e-01	0.05	3.5e-01	9.6e-01	▁▁▁▁▇
diet_Vitamins_vit_b6_pyridoxine	2	0	9.6e-01	0.00	9.6e-01	9.6e-01	▁▁▇▁▁
diet_Vitamins_vit_b7_biotin	1	0	9.9e-01	0.02	7.2e-01	1.0e+00	▁▁▁▁▇
diet_Vitamins_vit_b7_biotin	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Vitamins_folic_acid	1	0	9.9e-01	0.07	5.0e-02	1.0e+00	▁▁▁▁▇
diet_Vitamins_folic_acid	2	0	1.0e+00	0.00	1.0e+00	1.0e+00	▁▁▇▁▁
diet_Vitamins_vit_b12	1	0	4.9e-01	0.07	4.5e-01	1.0e+00	▇▅▁▁▁
diet_Vitamins_vit_b12	2	0	5.6e-01	0.00	5.6e-01	5.6e-01	▁▁▇▁▁
diet_Vitamins_vit_c	1	0	9.7e-01	0.04	6.5e-01	1.0e+00	▁▁▁▁▇
diet_Vitamins_vit_c	2	0	9.3e-01	0.00	9.3e-01	9.3e-01	▁▁▇▁▁
diet_Vitamins_beta_carotene	1	0	2.1e-01	0.02	1.8e-01	2.4e-01	▅▁▁▇▁
diet_Vitamins_beta_carotene	2	0	1.8e-01	0.00	1.8e-01	1.8e-01	▁▁▇▁▁
diet_Vitamins_lycopene	1	0	4.0e-01	0.06	3.2e-01	6.5e-01	▅▇▁▁▁
diet_Vitamins_lycopene	2	0	3.2e-01	0.00	3.2e-01	3.2e-01	▁▁▇▁▁

view_tree(phylo_clean) + ggtitle("Genus-Level OTUs")

Extract and Save Clean Phyloseq Objects

Exporting intermediate objects saves time if you need to re‐run later steps without re‐processing.

phylo_melt_clean     <- psmelt(phylo_clean)
clean_tree           <- phy_tree(phylo_clean)
save(phylo_clean     , file = "microeco/loris/phyloseq_genus.RData")
save(clean_tree      , file = "microeco/loris/tree_genus.RData")
save(phylo_melt_clean, file = "microeco/loris/phyloseq_melt_genus.RData")

Subset Additional Taxonomic Levels

Working at multiple ranks lets you compare community structure from broad (Phylum) to fine (Genus) scales in the same pipeline.

fam_phylo <- phylo_clean %>% tax_glom("Family")
ord_phylo <- phylo_clean %>% tax_glom("Order")
cla_phylo <- phylo_clean %>% tax_glom("Class")
phy_phylo <- phylo_clean %>% tax_glom("Phylum")

view_tree(fam_phylo) + ggtitle("Family-Level OTUs") 

view_tree(ord_phylo) + ggtitle("Order-Level OTUs")

view_tree(cla_phylo, "Class") + ggtitle("Class-Level OTUs") + 
      coord_cartesian(clip = "off") + 
      theme(plot.margin = margin(5, 65, 5, 5, "pt"))

view_tree(phy_phylo, "Phylum") + ggtitle("Phylum-Level OTUs") +  
      coord_cartesian(clip = "off") + 
      theme(plot.margin = margin(5, 65, 5, 5, "pt"))

Extract and Save for Other Levels

fam_phylo_melt     <- psmelt(fam_phylo)
ord_phylo_melt     <- psmelt(ord_phylo)
cla_phylo_melt     <- psmelt(cla_phylo)
phy_phylo_melt     <- psmelt(phy_phylo)

fam_tree           <- phy_tree(fam_phylo)
ord_tree           <- phy_tree(ord_phylo)
cla_tree           <- phy_tree(cla_phylo)
phy_tree           <- phy_tree(phy_phylo)

save(fam_phylo     , file = "microeco/loris/phyloseq_family.RData")
save(ord_phylo     , file = "microeco/loris/phyloseq_order.RData")
save(cla_phylo     , file = "microeco/loris/phyloseq_class.RData")
save(phy_phylo     , file = "microeco/loris/phyloseq_phylum.RData")

save(fam_tree      , file = "microeco/loris/tree_family.RData")
save(ord_tree      , file = "microeco/loris/tree_order.RData")
save(cla_tree      , file = "microeco/loris/tree_class.RData")
save(phy_tree      , file = "microeco/loris/tree_phylum.RData")

save(fam_phylo_melt, file = "microeco/loris/phyloseq_melt_family.RData")
save(ord_phylo_melt, file = "microeco/loris/phyloseq_melt_order.RData")
save(cla_phylo_melt, file = "microeco/loris/phyloseq_melt_class.RData")
save(phy_phylo_melt, file = "microeco/loris/phyloseq_melt_phylum.RData")

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Next Steps

In the microbiome_metrics tutorial you will learn how to choose and apply rarefaction or relative-abundance normalization, and then calculate fundamental diversity statistics (α-diversity indices, β-diversity distances, UniFrac, etc.). Refer to that section to decide when and how to rarefy your merged tables, or to work directly on normalized counts for non-phylogenetic metrics.